ASPEN: An Adaptive Spectral Physics-Enabled Network for Ginzburg-Landau Dynamics

ArXi:2512.03290v4 Announce Type: replace Physics-Informed Neural Networks (PINNs) have emerged as a powerful, mesh-free paradigm for solving partial differential equations (PDEs). However, they notoriously struggle with stiff, multi-scale, and nonlinear systems due to the inherent spectral bias of standard multilayer perceptron (MLP) architectures, which prevents them from adequately representing high-frequency components. In this work, we